Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems

2010 ◽  
Vol 15 (11) ◽  
pp. 2127-2140 ◽  
Author(s):  
Hui Wang ◽  
Zhijian Wu ◽  
Shahryar Rahnamayan
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
High Dimensional ◽  
Continuous Optimization Problems

scholarly journals Enhancing Differential Evolution on Continuous Optimization Problems by Detecting Promising Leaders

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 226557-226578
Author(s):  
Yiqiao Cai ◽  
Duanwei Wu ◽  
Shaopeng Liu ◽  
Shunkai Fu ◽  
Peizhong Liu
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Continuous Optimization Problems

scholarly journals Binary social group optimization algorithm for solving 0-1 knapsack problem

2022 ◽  
Vol 11 (1) ◽  
pp. 55-72 ◽  
Author(s):  
Anima Naik ◽  
Pradeep Kumar Chokkalingam
Keyword(s):  
Knapsack Problem ◽  
Social Group ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
High Dimensional ◽  
The Social ◽  
Social Group Optimization ◽  
Low Dimensional ◽  
Continuous Optimization Problems

In this paper, we propose the binary version of the Social Group Optimization (BSGO) algorithm for solving the 0-1 knapsack problem. The standard Social Group Optimization (SGO) is used for continuous optimization problems. So a transformation function is used to convert the continuous values generated from SGO into binary ones. The experiments are carried out using both low-dimensional and high-dimensional knapsack problems. The results obtained by the BSGO algorithm are compared with other binary optimization algorithms. Experimental results reveal the superiority of the BSGO algorithm in achieving a high quality of solutions over different algorithms and prove that it is one of the best finding algorithms especially in high-dimensional cases.

scholarly journals An Adaptive Differential Evolution Algorithm with Restart for Solving Continuous Optimization Problems

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Continuous Optimization ◽  
General Purpose ◽  
Adaptive Parameter ◽  
Adaptive Differential Evolution ◽  
Use Efficiency ◽  
Evolution Algorithm ◽  
Continuous Optimization Problems

A new adaptive differential evolution algorithm with restart (ADE-R) is proposed as a general-purpose method for solving continuous optimization problems. Its design aims at simplicity of use, efficiency and robustness. ADE-R simulates a population evolution of real vectors using vector mixing operations with an adaptive parameter control based on the switching of two selected intervals of values for each scaling factor and crossover rate of the basic differential evolution algorithm. It also incorporates a restart technique to supply new contents to the population to prevent premature convergence and stagnation. The method is tested on several benchmark functions covering various types of functions and compared with some well-known and state-of-art methods. The experimental results show that ADE-R is effective and outperforms the compared methods.

scholarly journals A Novel Tournament Selection Based Differential Evolution Variant for Continuous Optimization Problems

2015 ◽  
Vol 2015 ◽  
pp. 1-21 ◽  
Author(s):  
Qamar Abbas ◽  
Jamil Ahmad ◽  
Hajira Jabeen
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Convergence Speed ◽  
Local Optima ◽  
De Algorithm ◽  
Parent Selection ◽  
Tournament Selection ◽  
Overall Performance ◽  
Continuous Optimization Problems

Differential evolution (DE) is a powerful global optimization algorithm which has been studied intensively by many researchers in the recent years. A number of variants have been established for the algorithm that makes DE more applicable. However, most of the variants are suffering from the problems of convergence speed and local optima. A novel tournament based parent selection variant of DE algorithm is proposed in this research. The proposed variant enhances searching capability and improves convergence speed of DE algorithm. This paper also presents a novel statistical comparison of existing DE mutation variants which categorizes these variants in terms of their overall performance. Experimental results show that the proposed DE variant has significance performance over other DE mutation variants.

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